OpenRaiser/Intern-Atlas huggingface dataset:
Practical Decentralized Attribute-Based Delegation using Secure Name Systems
Venue: arXiv cs.CR 2018
Year: 2018

Identity and trust in the modern Internet are centralized around an oligopoly of identity service providers consisting solely of major tech companies. The problem with centralizing trust has become evident in recent discoveries of mass surveillance and censorship programs as well as information leakage through hacking incidents. One approach to decentralizing trust is distributed, attribute-based access control via attribute-based delegation (ABD). Attribute-based delegation allows a large number of cross-domain attribute issuers to be used in making authorization decisions. Attributes are not only issued to identities, but can also be delegated to other attributes issued by different entities in the system. The resulting trust chains can then be resolved by any entity given an appropriate attribute storage and resolution system. While current proposals often fail at the practicability, we show how attribute-based delegation can be realized on top of the secure GNU Name System (GNS) to solve an authorization problem in a real-world scenario.
Towards In-transit Analysis on Supercomputing Environments
Venue: arXiv cs.DC 2018
Year: 2018

The drive towards exascale computing is opening an enormous opportunity for more realistic and precise simulations of natural phenomena. The process of simulation, however, involves not only the numerical computation of predictions but also the analysis of results both to evaluate the simulation quality and interpret the simulated phenomenon. In this context, one may consider the duality between transaction and analytical processing to be repositioned in this new context. The co-habitation of simulation computation and analysis has been named after in situ analysis, whereas the separation in different systems considered as in-transit analysis. In this paper we focus in the latter model and study the impact of transferring varying block size data from the simulation system to the analytical one. We use the Remote Direct Memory Access protocol (RDMA) that reduces the interference on performance caused by data copies and context switching. It adopts an in-memory data transfer strategy combined with TCP, using the BSD sockets API and the Linux splice(2) syscall. We present a performance evaluation with our work and traditional utilities.
Regularized Finite Dimensional Kernel Sobolev Discrepancy
Venue: arXiv cs.LG 2018
Year: 2018

We show in this note that the Sobolev Discrepancy introduced in Mroueh et al in the context of generative adversarial networks, is actually the weighted negative Sobolev norm $||.||_{\dot{H}^{-1}(ν_q)}$, that is known to linearize the Wasserstein $W_2$ distance and plays a fundamental role in the dynamic formulation of optimal transport of Benamou and Brenier. Given a Kernel with finite dimensional feature map we show that the Sobolev discrepancy can be approximated from finite samples. Assuming this discrepancy is finite, the error depends on the approximation error in the function space induced by the finite dimensional feature space kernel and on a statistical error due to the finite sample approximation.
Weight Initialization in Neural Language Models
Venue: arXiv cs.CL 2018
Year: 2018

Semantic Similarity is an important application which finds its use in many downstream NLP applications. Though the task is mathematically defined, semantic similarity's essence is to capture the notions of similarity impregnated in humans. Machines use some heuristics to calculate the similarity between words, but these are typically corpus dependent or are useful for specific domains. The difference between Semantic Similarity and Semantic Relatedness motivates the development of new algorithms. For a human, the word car and road are probably as related as car and bus. But this may not be the case for computational methods. Ontological methods are good at encoding Semantic Similarity and Vector Space models are better at encoding Semantic Relatedness. There is a dearth of methods which leverage ontologies to create better vector representations. The aim of this proposal is to explore in the direction of a hybrid method which combines statistical/vector space methods like Word2Vec and Ontological methods like WordNet to leverage the advantages provided by both.
Facebook Reaction-Based Emotion Classifier as Cue for Sarcasm Detection
Venue: arXiv cs.CL 2018
Year: 2018

Online social media users react to content in them based on context. Emotions or mood play a significant part of these reactions, which has filled these platforms with opinionated content. Different approaches and applications to make better use of this data are continuously being developed. However, due to the nature of the data, the variety of platforms, and dynamic online user behavior, there are still many issues to be dealt with. It remains a challenge to properly obtain a reliable emotional status from a user prior to posting a comment. This work introduces a methodology that explores semi-supervised multilingual emotion detection based on the overlap of Facebook reactions and textual data. With the resulting emotion detection system we evaluate the possibility of using emotions and user behavior features for the task of sarcasm detection. More than 1 million English and Chinese comments from over 62,000 public Facebook pages posts have been collected and processed, conducted experiments show acceptable performance metrics.
End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition
Venue: arXiv cs.LG 2018
Year: 2018

In this paper we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches with a single kernel in each layer. We develop an algorithm for simultaneously learning all the kernels from the training data. Our approach dubbed Deep Tensor Decomposition (DeepTD) is based on a rank-1 tensor decomposition. We theoretically investigate DeepTD under a realizable model for the training data where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted convolutional kernels. We show that DeepTD is data-efficient and provably works as soon as the sample size exceeds the total number of convolutional weights in the network. We carry out a variety of numerical experiments to investigate the effectiveness of DeepTD and verify our theoretical findings.
Deep Reinforcement Learning for Resource Management in Network Slicing
Venue: arXiv cs.NI 2018
Year: 2018

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.
DroidMark: A Tool for Android Malware Detection using Taint Analysis and Bayesian Network
Venue: arXiv cs.CR 2018
Year: 2018

With the increasing user base of Android devices and advent of technologies such as Internet Banking, delicate user data is prone to be misused by malware and spyware applications. As the app developer community increases, the quality reassurance could not be justified for every application and a possibility of data leakage arises. In this research, with the aim to ensure the application authenticity, Deep Learning methods and Taint Analysis are deployed on the applications. The detection system named DroidMark looks for possible sinks and sources of data leakage in the application by modelling Android lifecycle and callbacks, which is done by Reverse Engineering the APK, further monitoring the suspected processes and collecting data in different states of the application. DroidMark is thus designed to extract features from the applications which are fed to a trained Bayesian Network for classification of Malicious and Regular applications. The results indicate a high accuracy of 96.87% and an error rate of 3.13% in the detection of Malware in Android devices.
Payload-size and Deadline-aware Scheduling for Upcoming 5G Networks: Experimental Validation in High-load Scenarios
Venue: arXiv cs.NI 2018
Year: 2018

High data rates, low latencies, and a widespread availability are the key properties why current cellular network technologies are used for many different applications. However, the coexistence of different data traffic types in the same 4G/5G-based public mobile network results in a significant growth of interfering data traffic competing for transmission. Particularly in the context of time-critical and highly dynamic Cyber Physical Systems (CPS) and Vehicle-to-Everything (V2X) applications, the compliance with deadlines and therefore the efficient allocation of scarce mobile radio resources is of high importance. Hence, scheduling solutions are required offering a good trade-off between the compliance with deadlines and a spectrum-efficient allocation of resources in mobile networks. In this paper, we present the results of an experimental validation of the Payload-size and Deadline-aware (PayDA) scheduling algorithm using a Software-Defined Radio (SDR)-based eNodeB. The results of the experimental validation prove the high efficiency of the proposed PayDA scheduling algorithm for time-critical applications in both miscellaneous and homogeneous data traffic scenarios.
Constructing Customized Multi-Hop Topologies in Dense Wireless Network Testbeds
Venue: arXiv cs.NI 2018
Year: 2018

Testbeds are a key element in the evaluation of wireless multi-hop networks. In order to relieve researchers from the hassle of deploying their own testbeds, remotely controllable testbeds, such as the FIT/IoT-LAB, are built. However, while the IoT-LAB has a high number of nodes, they are deployed in constraint areas. This, together with the complex nature of radio propagation, makes an ad-hoc construction of multi-hop topologies with a high number of hops difficult. This work presents a strategic approach to solve this problem and proposes algorithms to generate topologies with desired properties. The implementation is evaluated for the IoT-LAB testbeds and is provided as open-source software. The results show that preset topologies of various types can be built even in dense wireless testbeds.
Fuzzy Membership Function Implementation with Memristor
Venue: arXiv cs.ET 2018
Year: 2018

The neuro-fuzzy system is network which resemble human-like operation of the naturally imprecise data and decision-making. This paper proposes implementation of the fundamental module of the neuro-fuzzy system - membership function (MF), realized as a analog electronic hardware. The memristive crossbar arrays are used as the architecture for proposed MF analog circuit. The main advantages of the memristive crossbar circuit are size, energy efficiency and fault tolerance compared to another analog alternatives. The conducted crossbar SPICE simulation with MS model of the memristor results confirm the performance and highlighted benefits of the proposed circuit.
Identifying Object States in Cooking-Related Images
Venue: arXiv cs.CV 2018
Year: 2018

Understanding object states is as important as object recognition for robotic task planning and manipulation. To our knowledge, this paper explicitly introduces and addresses the state identification problem in cooking related images for the first time. In this paper, objects and ingredients in cooking videos are explored and the most frequent objects are analyzed. Eleven states from the most frequent cooking objects are examined and a dataset of images containing those objects and their states is created. As a solution to the state identification problem, a Resnet based deep model is proposed. The model is initialized with Imagenet weights and trained on the dataset of eleven classes. The trained state identification model is evaluated on a subset of the Imagenet dataset and state labels are provided using a combination of the model with manual checking. Moreover, an individual model is fine-tuned for each object in the dataset using the weights from the initially trained model and object-specific images, where significant improvement is demonstrated.
Hierarchical Reinforcement Learning with Deep Nested Agents
Venue: arXiv cs.AI 2018
Year: 2018

Deep hierarchical reinforcement learning has gained a lot of attention in recent years due to its ability to produce state-of-the-art results in challenging environments where non-hierarchical frameworks fail to learn useful policies. However, as problem domains become more complex, deep hierarchical reinforcement learning can become inefficient, leading to longer convergence times and poor performance. We introduce the Deep Nested Agent framework, which is a variant of deep hierarchical reinforcement learning where information from the main agent is propagated to the low level $nested$ agent by incorporating this information into the nested agent's state. We demonstrate the effectiveness and performance of the Deep Nested Agent framework by applying it to three scenarios in Minecraft with comparisons to a deep non-hierarchical single agent framework, as well as, a deep hierarchical framework.
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs
Venue: arXiv cs.AI 2018
Year: 2018

Checking various log files from different processes can be a tedious task as these logs contain lots of events, each with a (possibly large) number of attributes. We developed a way to automatically model log files and detect outlier traces in the data. For that we extend Dynamic Bayesian Networks to model the normal behavior found in log files. We introduce a new algorithm that is able to learn a model of a log file starting from the data itself. The model is capable of scoring traces even when new values or new combinations of values appear in the log file.
Proofs and Programs about Open Terms
Venue: arXiv cs.PL 2018
Year: 2018

Formal deductive systems are very common in computer science. They are used to represent logics, programming languages, and security systems. Moreover, writing programs that manipulate them and that reason about them is important and common. Consider proof assistants, language interpreters, compilers and other software that process input described by formal systems. This thesis shows that contextual types can be used to build tools for convenient implementation and reasoning about deductive systems with binders. We discuss three aspects of this: the reconstruction of implicit parameters that makes writing proofs and programs with dependent types easier, the addition of contextual objects to an existing programming language that make implementing formal systems with binders easier, and finally, we explore the idea of embedding the logical framework LF using contextual types in fully dependently typed theory. These are three aspects of the same message: programming using the right abstraction allows us to solve deeper problems with less effort. In this sense we want: easier to write programs and proofs (with implicit parameters), languages that support binders (by embedding a syntactic framework using contextual types), and the power of the logical framework LF with the expressivity of dependent types.
Deterministic Distributed Ruling Sets of Line Graphs
Venue: arXiv cs.DS 2018
Year: 2018

An $(α,β)$-ruling set of a graph $G=(V,E)$ is a set $R\subseteq V$ such that for any node $v\in V$ there is a node $u\in R$ in distance at most $β$ from $v$ and such that any two nodes in $R$ are at distance at least $α$ from each other. The concept of ruling sets can naturally be extended to edges, i.e., a subset $F\subseteq E$ is an $(α,β)$-ruling edge set of a graph $G=(V,E)$ if the corresponding nodes form an $(α,β)$-ruling set in the line graph of $G$. This paper presents a simple deterministic, distributed algorithm, in the $\mathsf{CONGEST}$ model, for computing $(2,2)$-ruling edge sets in $O(\log^* n)$ rounds. Furthermore, we extend the algorithm to compute ruling sets of graphs with bounded diversity. Roughly speaking, the diversity of a graph is the maximum number of maximal cliques a vertex belongs to. We devise $(2,O(\mathcal{D}))$-ruling sets on graphs with diversity $\mathcal{D}$ in $O(\mathcal{D}+\log^* n)$ rounds. This also implies a fast, deterministic $(2,O(\ell))$-ruling edge set algorithm for hypergraphs with rank at most $\ell$. Furthermore, we provide a ruling set algorithm for general graphs that for any $B\geq 2$ computes an $\big(α, α\lceil \log_B n \rceil \big)$-ruling set in $O(α\cdot B \cdot \log_B n)$ rounds in the $\mathsf{CONGEST}$ model. The algorithm can be modified to compute a $\big(2, β\big)$-ruling set in $O(βΔ^{2/β} + \log^* n)$ rounds in the $\mathsf{CONGEST}$~ model, which matches the currently best known such algorithm in the more general $\mathsf{LOCAL}$ model.
Scanner: Efficient Video Analysis at Scale
Venue: arXiv cs.CV 2018
Year: 2018

A growing number of visual computing applications depend on the analysis of large video collections. The challenge is that scaling applications to operate on these datasets requires efficient systems for pixel data access and parallel processing across large numbers of machines. Few programmers have the capability to operate efficiently at these scales, limiting the field's ability to explore new applications that leverage big video data. In response, we have created Scanner, a system for productive and efficient video analysis at scale. Scanner organizes video collections as tables in a data store optimized for sampling frames from compressed video, and executes pixel processing computations, expressed as dataflow graphs, on these frames. Scanner schedules video analysis applications expressed using these abstractions onto heterogeneous throughput computing hardware, such as multi-core CPUs, GPUs, and media processing ASICs, for high-throughput pixel processing. We demonstrate the productivity of Scanner by authoring a variety of video processing applications including the synthesis of stereo VR video streams from multi-camera rigs, markerless 3D human pose reconstruction from video, and data-mining big video datasets such as hundreds of feature-length films or over 70,000 hours of TV news. These applications achieve near-expert performance on a single machine and scale efficiently to hundreds of machines, enabling formerly long-running big video data analysis tasks to be carried out in minutes to hours.
The Role of Models and Megamodels at Runtime
Venue: arXiv cs.SE 2018
Year: 2018

In model-driven software development a multitude of interrelated models are used to systematically realize a software system. This results in a complex development process since the models and the relations between the models have to be managed. Similar problems appear when following a model-driven approach for managing software systems at runtime. A multitude of interrelated runtime models are employed simultaneously, and thus they have to be maintained at runtime. While for the development case megamodels have emerged to address the problem of managing models and relations, the problem is rather neglected for the case of runtime models by applying ad-hoc solutions. Therefore, we propose to utilize megamodel concepts for the case of multiple runtime models. Based on the current state of research, we present a categorization of runtime models and conceivable relations between them. The categorization describes the role of interrelated models at runtime and demonstrates that several approaches already employ multiple runtime models and relations. Then, we show how megamodel concepts help in organizing and utilizing runtime models and relations in a model-driven manner while supporting a high level of automation. Finally, the role of interrelated models and megamodels at runtime is discussed for self-adaptive software systems and exemplified by a case study.
LECTOR Based Clock Gating for Low Power Multi-Stage Flip Flop Applications
Venue: arXiv cs.AR 2018
Year: 2018

Power dissipation in integrated circuits is one of the major concerns to the research community, at the verge when more number of transistors are integrated on a single chip. The substantial source of power dissipation in sequential elements of the integrated circuit is due to the fast switching of high frequency clock signals. These signals do not carry any information and are mainly intended to synchronize the operation of sequential components. This unnecessary switching of Clock, during the HOLD phase of either logic 1 or logic 0, may be eliminated using a technique, called Clock Gating. In this paper, we have incorporated a recent clock gating style called LECTOR based clock gating LB CG to drive multi stage architecture and simulated its performance using 90nm CMOS Predictive Technology Model PTM with a power supply of 1.1V at 18GHz clock frequency. A substantial savings in terms of average power in comparison to its non gated correspondent have been observed.
Reduction of power grid fluctuations by communication between smart devices
Venue: arXiv cs.SY 2018
Year: 2018

The increase of electric demand and the progressive integration of renewable sources threatens the stability of the power grid. To solve this issue, several methods have been proposed to control the demand side instead of increasing the spinning reserve on the supply side. Here we focus on dynamic demand control (DDC), a method in which appliances can delay its scheduled operation if the electric frequency is outside a suitable range. We have recently shown that DDC effectively reduces small and medium-size frequency fluctuations but, due to the need of recovering pending tasks, the probability of large demand peaks, and hence large frequency fluctuations, may actually increase. Although these events are very rare they can potentially trigger a failure of the system and therefore strategies to avoid them have to be addressed. In this work, we introduce a new method including communication among DDC devices belonging to a given group, such that they can coordinate opposite actions to keep the group demand more stable. We show that for this method the amount of pending tasks decreases by a factor 10 while large frequency fluctuations are significantly reduced or even completely avoided.